Running list of conjectures about neural networks 📜:

It's a big day for understanding how LLMs generalize from their training signals!

Such a simple, yet ridiculous-sounding method. It has every right to work this well.

You literally just add a prompt (or some other intervention like a steering vector) that explains-away the unwanted pattern of generalization. That's it.

a.k.a. the unreasonable effectiveness of inoculation

*taps the sign*

This is the most impressive release I’ve seen in a while. Fully open suite, from the start of training to multiple endpoints (chat, reasoning, domain-specific RL), with every dataset used along the way. Incredible research potential here.

When doing independent evals for open-weight releases: You can use the API from the developer, but what if it trains on your data? You can use third-party infra, but what if that is buggy/incorrect at first? And waiting for those to stabilize means you’re slow to publish.

at the suggestion of @CFGeek & @joel_bkr, i'm running a manifundraiser for my model tinkering! it's already passed the minimum goal of $5k, but has stretch goals for funding more open-ended research. if that interests you, you can find it here: https://t.co/kQYe3x79To

Announcing Olmo 3, a leading fully open LM suite built for reasoning, chat, & tool use, and an open model flow—not just the final weights, but the entire training journey. Best fully open 32B reasoning model & best 32B base model. 🧵

A little visibility into how AI companies like OpenAI work with external assessors. It even includes snippets from their legal agreements!

METR completed a pre-deployment evaluation of GPT-5.1-Codex-Max & found its capabilities consistent with past trends. If our projections hold, we expect further OpenAI development in the next 6 months is unlikely to pose catastrophic risk via automated AI R&D or rogue autonomy.

Great guest lecture from @SuryaGanguli’s theoretical neuroscience course. Felt like a complete impostor taking it years ago, but it was one of those classes that expands your mind even if you’re drowning in the material.

Regrettable “own goal” how AI safety folks made an enemy out of open-source a few years back. Lots of the technical problems in safety & security would benefit from greater transparency around how models are developed (e.g. attribution, evaluation, interpretability, verification)

This offers an interesting perspective on how hippocampal learning complements cortical: cortical learning may be overly tied to explicit learning goals/formulations, while episodic retrieval of learning experiences can allow using latent information more flexibly. 7/

I was honored to speak at Princeton’s symposium on The Physics of John Hopfield: Learning & Intelligence this week. I sketched out a perspective that ties together some of our recent work on ICL vs. parametric learning, and some possible links to hippocampal replay: 1/

I would like to introduce the concept of a phenome-wide association study to my LLM-interpreting friends. It's where you start with a genetic variant, and figure out which phenotypes are associated with it (the inverse of figuring out which variants are associated with a trait).

Today, I write about US-China competition in AI and associated technologies as I see it. “Race” is a fairly poor analogy, in my view, for understanding the magnitude of what is underway.

Can somebody with a cybersecurity background weigh in on how big of a deal this is? Just finished the report, but I didn’t feel like I learned much from it.

This is very cool!

LLMs are notorious for "hallucinating": producing confident-sounding answers that are entirely wrong. But with the right definitions, we can extract a semantic notion of "confidence" from LLMs, and this confidence turns out to be calibrated out-of-the-box in many settings (!)